Download Social Capital, Investment and Economic Growth: Evidence for Spanish Provinces Jesús Peiró-Palomino

Social Capital, Investment and Economic Growth:
Evidence for Spanish Provinces
Emili Tortosa-Ausina†
Jesús Peiró-Palomino∗
Abstract
This article analyzes the role of social capital on economic growth in the Spanish
provinces during the 1983–2005 period. Whereas most studies have been using survey
data in order to measure social capital, we use a measure whose construction is based on
similar criteria as other measures of capital stock. In addition, compared with more standard measures of social capital and trust, our measure is available with a high level of
disaggregation and with annual frequency for a long time span. Following a panel data
approach, we determine upon the case of the Spanish provinces that social capital impacts
positively on GDP per capita, highlighting that social features are important for explaining the differences in wealth that we can observe for the different Spanish provinces. We
also try to determine the transmission mechanisms from social capital to growth, finding a
highly positive relation between social capital and private physical investment.
Keywords: social capital, GDP per capita, convergence, physical capital, investment.
Communications to: Jesús Peiró-Palomino, Departament d’Economia, Universitat Jaume I,
Campus del Riu Sec, 12071 Castelló de la Plana, Spain. Tel.: +34 964388615, fax: +34 964728591,
e-mail: peirojuji.es
∗ Universitat
† Universitat
Jaume I.
Jaume I and Ivie.
1
1.
Introduction
Traditionally, economic growth has been one of the topics which have attracted more interest in the economic literature. The first steps in the matter are attributed to Solow (1957),
who proposed a model including physical capital investment, labor and technological change.
Subsequently, the economic growth literature has considered a large set of possible explanatory variables of a different nature, such as human capital or regional, political, religious
and social variables. Among the most important contributions subsequent to Solow’s work,
we find those which found evidence in favor of human capital as an additional explanatory
factor (Barro, 1991; Mankiw et al., 1992; Nonneman and Vanhoudt, 1996). Despite the considerable efforts for determining which are really the robust factors behind the economic growth
(Levine and Renelt, 1992; Sala-i-Martin, 1997; Crespo-Cuaresma et al., 2011), a consensus has
not been reached yet.
In the last few years, a new variable has been considered by several studies on this issue:
social capital. This concept was introduced by Coleman (1988) more than two decades ago. In
the context of empirical economic growth and convergence, the analyst would be confronted
with evaluating if social features such as trust, associationism, social participation or publicspiritedness influence on the economic performance of one region, or how important might
the social component be.
However, despite the importance of this type of issue is growing, scholars face up two important problems. The first one is what some authors refer to as the vagueness of the concept
(Torsvik, 2000). Social capital is a topic which stirs the interest not only of economists but also
sociologists and policymakers and, accordingly, the concept is characterized by an interdisciplinary nature. Although this might be a priori good, in practical terms it impedes a consensus
to be reached about the impact of social capital—both where and how it truly impacts. The
second problem scholars face when approaching the concept, and perhaps the most relevant
from a point of view of measuring how it affects growth and convergence, is that data on social
capital are relatively scarce, and that the data provided by different institutions usually carry
different meanings—and, therefore, the implications for growth and convergence may also
vary from one measure to the other. As we will see throughout the study, this is ultimately the
main reason for considering a social capital measure which has been constructed with similar
underpinnings to those used for building other databases such as physical or human capital.
Considering the particular discipline of economics, over the last few years some studies
have been analyzing how social capital affects different dimensions of economic activity in
different countries and regions. In this line of research, Beugelsdijk and Van Schaik (2005)
have reported evidence for Europe using a sample of 54 European regions, considering the
information of the European Values Survey (EVS) indicators for both trust and associational
2
activity. They suggest that trust is not related to growth but rather point out a strong relationship between active groups and growth.1 Previously, Knack and Keefer (1997) had found for a
sample of 29 countries and using data from World Values Survey (WVS) a strong relationship
between trust and civic cooperation and growth but, contrary to Beugelsdijk and Van Schaik
(2005), the relation between associational activity and growth was not significant. Some other
studies have been focused on developing countries such as Narayan and Pritchett (1999) who,
using a sample of 1,376 household villages in rural Tanzania, along with data from the Social
Capital and Poverty Survey (SCPS),2 determined that higher levels of social capital provide
higher incomes.3 Portela and Neira (2002) compared social capital in Spain with European
countries finding lower endowments in Spain, employing trust and associational indicators
provided by WVS. They also found significant effects from social capital to growth in a sample
of European countries.
The studies cited in the previous paragraph are some examples of contributions reporting
interesting conclusions which might be useful to understand—for the particular case of Spain
on which we focus—why some provinces are systematically richer than others based on terms
of GDP per capita. Pérez (2007) concluded that all provinces without any exception have experienced an important economic growth in the period 1955–2005, but disparities among them
are still considerable today. It constitutes virtually a consensus for scholars who have studied
the Spanish case, the fact that convergence process in GDP per capita among the different
Spanish regions4 slowed down in the 1980s, whereas labor productivity followed a convergent
path. Dolado et al. (1994), Raymond Bara and García Greciano (1994), Rabadán et al. (1996),
Goerlich and Mas (2001), Goerlich et al. (2002), or Peña Sánchez (2008), constitute relevant contributions.
Pérez (2007) determined that the persistent encountered disparities are consequence of
the distinct capacity of the provinces for attracting activity, which is finally responsible for the
generation of income and employment. In that sense, studies such as Becattini (1979) or Trigilia
(2005) concluded that the existence of social capital in one territory is one of the factors for the
activity attraction and local development. Because the presence of social capital in one territory
can also impel the generation of other kinds of capital, such as human or physical capital
(Dearmon and Grier, 2011), it is feasible that the cited differences in the location of activity in
the Spanish provinces can be explained, at least partially, by differences in the endowments
of social capital. In addition, since Spanish economic development has been accompanied by
1 Specifically,
they included “active groups” whose components participated actively, and “passive groups”
where people is only part of the group but not active participants.
2 This database, along with the European Values Survey (EVS), the World Values Survey (WVS) and other
databases will be commented with more detail below.
3 They also concluded that social capital owned by a single household spreads its effects to the entire village.
4 We find studies basically with two distinct levels of disaggregation: NUTS 2 if the disaggregation is at Comunidades Autónomas level and NUTS 3 is that disaggregation is at Provinces level.
3
greater levels of physical investment (Pérez, 2007) and physical capital accumulation has been
considered by the literature as an important determinant of economic growth, we test as an
attempt of highlight how social capital affects the economy, if a causality relationship from
social capital to physical investment takes place at province level.
This paper is structured as follows. In the next section we present a revision of the vast
literature around the social capital concept and its measurement. Afterwards, we explain the
advantages of using Pérez et al. (2005) model of social capital generation and accumulation.
In section four, data, sources and some descriptive statistics are presented. In section five
we test if social capital has a significant role on economic development. We also provide
some evidence in favor that investment could be one of the transmission mechanisms from
social capital to growth. At the end of the section robustness of the results is tested providing
jackknife estimations. Finally, section six concludes.
2.
Social capital literature review
2.1.
Two different approaches for the same concept
The concept of social capital can be examined from different perspectives. A great number
of contributions deal with the concept itself and its impact on a variety of fields. It is widely
accepted among scholars that social capital contributes to reduce transaction costs and affects positively economic development, among other beneficial effects. However, nowadays,
despite the large number of studies (Helliwell and Putnam, 1995; Knack and Keefer, 1997;
Beugelsdijk and Van Schaik, 2005), there is no agreement as to which definition, approach
or methodology is the most appropriate to determine its effects. Durlauf (2002) has expressed
his disagreement with the techniques used in some of the most relevant studies on the matter.
The majority of the most influential contributions are dated in the past decade. Robert
Putnam, in his seminal study entitled “Making Democracy Work” (1993), analyzed the effect
of social capital for explaining the differences in economic development and institutional performance in the Northern and Southern regions of Italy. The main conclusion he arrived to
was that social capital partly explains the large differences between the North and South of
Italy in terms of institutional performance and economic development.
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Other authors have
focused their interest on testing whether Putnam’s results can be generalized using a sample
of countries (Schneider et al., 2000), finding some conflicting results.
We can find two distinct views for explaining the origins of social capital. Jackman and Miller
(1998) compiled and discussed the different social capital approaches. They argued that the
pioneering social capital studies employed an endogenous approach of the concept. That view
5 Putnam
also analyzed the American case concluding that social capital has been declining in the last decades
(Putnam, 1995).
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implies that social capital is born inside the individual and the organizations. Be A and B
two representative individuals in one determined society, Coleman (1988) defined trust as the
expectation created in A of being corresponded by B when A makes something for B. This
would imply that a stock of social capital in a given society can be created by the accumulation
of reciprocal trust relationships. Coleman (1988) also argued that information is needed in
providing a basis for trusting the others.6 Another relevant factor is the penalties imposed
if one individual acts in opportunistic way.7 Opportunistic behavior may imply an exclusion
and the impossibility to participate in the aggregated benefits that social capital provides.8
Thereby, trust to the long term is also viewed as an instrument to reach a cooperative solution
in a context of the Prisoner’s Dilemma (Torsvik, 2000).9
In contrast, there is an exogenous view of the concept, which stresses that social capital
is not a personal cooperative decision but a structural element of the society created by a
confluence of certain cultural values, religion, political system, past and current institutions
and social structure. A considerable number of studies underlying the role of “cultural matters” in explaining economic development has included these aspects in the traditional models of economic growth. Whereas both of the mentioned views are incompatible for some
authors like Jackman and Miller (1998), others have not made that distinction, combining different endogenous and exogenous aspects. Let’s see, for instance, Knack and Keefer (1995),
Knack and Keefer (1997), Keefer and Knack (1997), Keefer and Knack (2002), Putnam (1995),
Helliwell and Putnam (1995), Akçomak and Ter Weel (2009), La Porta et al. (1997), Fukuyama
(1995), or Granato et al. (1996a).
In Knack and Keefer (1997) an index for measuring the effect of social capital on economic
development is constructed. It consist of three endogenous variables: (i) trust; (ii) associational activity; and (iii) a civic index. These are traditionally, three of the most used variables
as proxies for social capital in the literature. They found strong significant effects for trust and
civic cooperation but no significant relationship between associational activity and economic
performance. They also found evidence that low social polarization and formal institutional
rules that constrain the government from acting arbitrarily are related to the creation of trust
and cooperative norms. So, they argued that exogenous factors are the determinants of social
capital proxies. The contradiction for Jackman and Miller (1998) is that the idea of the pioneering studies of social capital, leaded by Coleman (1988), considered trust and cooperative
6 In a society with certain and clear information, making decisions is easier and securer because individuals can
check all the important variables they need to know to make a decision.
7 The nature of these penalties may be formal (laws and regulations) or informal (social cost imposed to opportunistic actors). The last one would be closely related with social capital.
8 Exclusion has a damaging effect not only on the excluded but on the global society.
9 In the classic iterated Prisioner’s Dilemma game, participants cooperate because they know that long-term
benefits of cooperation are higher than short-term benefits derived from deviations of the cooperative solution.
The nature and the mechanisms of the endogenous view are very close to this theory.
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behaviors as a product of an internal decision, not a consequence of environmental factors.
In Knack and Keefer (1995) they focused exclusively on the role of the institutions on the economic performance and the catching up effects, concluding that institutions which guarantee
the property rights are crucial for achieving better economic outcomes.
Within the exogenous view, we can find other authors whose research is focused on social
capital as a result of political regimes and policies (Paldam and Svendsen, 2000; Paxton, 2002;
Torcal and Montero, 2000; Granato et al., 1996b). Paldam and Svendsen (2000) concluded that
antidemocratic, communist societies lead to groups which act in their interest, and against
general interest. Rose (2000) studied for the case of Russia the different mechanisms used
by Russian citizens in order to cover those needs that the state cannot supply. Paxton (2002)
found evidence for explaining that the relationship between trust and democracy is reciprocal,
whereas Torcal and Montero (2000), in their research for the Spanish case, concluded that the
Spanish political transition in 1975 from dictatorship to democratic system did not create a
larger amount of social capital stock. This fact leads authors to conclude that Spain is anchored
in a low intensity social capital equilibrium. This low social capital stock is inherited, following
these authors, from generation to generation as a result of past political experiences.
Social capital implications can also reach the credit market. One of the most important
contributions is Guiso et al. (2004), who concluded that in countries or regions with high social
capital endowments their inhabitants can gain better access to credit since there is an increase
in the number of credit instruments used. Social capital also increases the transparency of the
available information and, putting all together, economic activity may be positively affected.
Until now, we have been focusing on the different views that the social capital literature
considers, and the different fields where the positive effects of social capital have been demonstrated. However, nothing about how social capital is spread inside one society has been said.
It is here when the concept of the network emerges. A high number of studies has stressed its
role (Coleman, 1988; Woolcock and Narayan, 2000; Paldam and Svendsen, 2000; Paxton, 2002;
Torsvik, 2000). The network is considered as the instrument which provides the connections
among the individuals, and allows for the diffusion of social capital.
According to Pérez et al. (2005), high trust societies are characterized by a high-density,
well connected network.10 The concept of network should be understood as the relationships
and ties between members in the society we are considering. If individuals in one society are
rich in terms of social capital but the network is not wide enough, then the positive effects that
social capital provides will not be achieved.
The above overview has shown that there is not a consensus on how social capital should be
understood. Only one thing seems clear: regardless of the approach followed, either endogenous or exogenous, in those areas where social capital is abundant, contracts and agreements
10 Societies
with isolated groups may be harmful for the creation of a social capital stock (Paxton, 2002).
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may be enforced with lower transaction costs. However, in spite of the advances in the knowledge of this issue, more evidence on the effects of social capital is needed—at least from the
point of view of some disciplines such as economics.
Yet it is not an easy task because the analysts are firstly confronted with the difficulties in
quantifying social capital itself, which have generated a relevant literature focused on the creation mechanisms of social capital, of which Fedderke et al. (1999), Torsvik (2000) or Woolcock
(1998) constituted relevant examples. Accordingly, in recent years there has been a growing
interest by scholars for determining and quantifying how important social capital is in order
to achieve certain levels of economic development.
2.2.
The measurement of social capital
Therefore, from the previous section it may be easily inferred that one of the major problems in
the study of social capital is its measurement. As indicated in the preceding paragraphs, several
authors have highlighted the difficulties in the measurement because its intangible nature. Two
of the measures traditionally used are the trust and associational activity indicators contained
in the World Values Survey (WVS) database,11 using what scholars have referred to as “the
generally speaking question”. Specifically, the question asked by the WVS in its surveys is:
“Generally speaking, would you say that most people can be trusted, or that you cannot be
too careful in dealing with people?”, with two possible answers: “most people can be trusted”,
or “can’t be too careful”. The WVS also provides a membership association indicator. These
indicators have been used in some of the major contributions in the field (Granato et al., 1996a;
Knack and Keefer, 1997; Zak and Knack, 2001).
Another indicator of widespread use is that contained in the European Value Studies (EVS)
database,12 where we can find regional human values data for a set of European countries.
The question is again the “generally speaking question”. In this case, though, answers are
not dichotomous but can vary from 1 (“you can’t be too careful”) to 10 (“most people can
be trusted”), providing more complete indicator. Group membership are deduced from two
questions: “During the last 12 months, have you done any of the following: (i) worked in a
political party or action group?” and “(ii) worked in another organization or association?”.
The two possible answers are “Yes” or “No”.
Other measures have also been utilized as proxies for social capital including political
participation, institutional variables, confidence in government, compound civic indicators
like (Knack and Keefer, 1997), or different items or questionnaires used to measure specifically social capital levels in a concrete region. A relevant study in this respect is that by
Narayan and Pritchett (1999), who constructed a measure from a “Social Capital and Poverty
11 See
12 See
http://www.worldvaluessurvey.org.
http://www.europeanvaluesstudy.eu.
7
Survey questionnaire” to test the role of social capital viewed from a domestic perspective.
Unfortunately, the measures reviewed in the preceding paragraphs have certain disadvantages which can jeopardize their use under some circumstances. First, they have a limited
coverage both in the dimensions of space (number of countries or regions included) and time
(years in the sample). Second, in the particular case we are dealing with, in which we attempt
to understand how social capital might have affected the growth profiles of the fifty Spanish provinces, the measures just reviewed do not provide the required level of disaggregation
(provinces, NUTS 3 in European terminology, which would also include the autonomous cities
of Ceuta and Melilla).13
In order to expand both the space and time dimensions of our data we will consider a
new measure of social capital constructed by the Ivie.14 This measure is available not only for
Spanish provinces and regions, but also for a large sample of countries and long time span,
which is updated on a regular basis. In addition, it has some additional features which make its
use quite attractive in this particular setting. We summarize its main characteristics in the next
section. This measure has already been used in recent studies applied to different contexts but
with aims related to ours such as Pastor and Tortosa-Ausina (2008) or Miguélez et al. (2011).
3.
Using an economic approach of social capital
As indicated above, an important stem of the literature has been devoted to measure the
impact of social capital on growth and, to a lesser extent, convergence using proxy data for
social capital drawn from surveys. In contrast to other measures of social capital such as those
reviewed in the preceding section, the measure we use is more elaborated one. We devote
this section to stress those of its features which are more relevant for our study in terms of its
suitability. Appendix A provides the most important mathematical relations of the measure.
For a more complete explanation, see (Pérez et al., 2005).
As discussed previously, data from surveys provided by WVS or EVS are not available
neither for the different Spanish provinces nor for the analyzed time period . In contrast to
the surveys described above, which are only available for certain years,15 the measure of social
capital we use provides data yearly, enabling the construction of a balanced panel data, which
can yields stronger conclusions. This is one of the reasons why we use the Ivie’s measure but
its use has other powerful advantages.
One of the most interesting features of the measure is that it deals with social capital as
13 As
indicated in the introduction, some studies such as Beugelsdijk and Van Schaik (2005) have analyzed social
capital issues for European regions; however, the level of disaggregation employed was far less detailed than that
corresponding to Spanish NUTS 3.
14 Instituto Valenciano de Investigaciones Económicas (www.ivie.es), in collaboration with the Banco Bilbao Vizcaya Foundation (FBBVA, www.fbbva.es).
15 There are different waves but frequency is not annual.
8
an asset in which people can invest. Solow (2000) disagreed with the idea that social capital
can be one of the drivers of economic activity, in part because he thought that social capital
cannot be seen as capital. Specifically, he claimed that the word capital is related to a stock of
production factors which is expected to yield productive services for some time.
Dasgupta and Serageldin (2001) suggested the plausibility of the construction of an index
of aggregated social capital, concluding also that additional research should follow in that direction. Glaeser et al. (2000) expressed that the traditional measures for social capital are not
the most suitable when we are in the economic field. They developed a model of individual social capital accumulation, acknowledging the existence of difficulties in the aggregation
at the society level. Therefore, this model cannot provide an answer when studying the differences among provinces, which are not individuals but communities of individuals and,
consequently, aggregation becomes essential. In the same line, Durlauf (2002) criticized the
lack of a theoretical framework for the determinants of social capital formation and accumulation and also pointed out the weakness of the studies which tests the importance of social
capital from a macroeconomic perspective.
The social capital measure we use provides an answer in this respect. The model of social
capital accumulation considered is based for its construction on similar ideas as models of
physical capital accumulation. This implies that social capital is understood as an additional
input in the production process and a stock of it is available for each society, which depreciates
over time as any other type of capital stock. Individuals invest in social capital because they
expect positive returns in the future derived from that investment. Ivie’s approach considers
that social capital provides services, and those services translate into a reduction of transaction
costs. That cost reduction conforms the final benefits of investing in social capital.
Another advantage of this approach is the importance that the measure devotes to the
economic aspects in the generation of social capital as opposed to other measures focusing on
social and cultural characteristics. Our approach considers the economic relationships such
as trade, employment, financing or income distribution as determinants of the incentives for
investing in social capital. Pérez et al. (2005) claim that the cited economic variables have
not been sufficiently considered by the literature of social capital, and that they could be even
more important than other social or cultural variables widely considered. The cited author also
gives several explanatory reasons justifying the dominance of social variables over economic
variables in the measurement of social capital. The main conclusion they arrive to is that
social capita generation cannot be exclusively confined to non-economic relationships, and
that economic relationships must also be taken into account, especially when we are dealing
with advanced economies with expectations of a continuous progress, which is precisely the
case of Spanish provinces.
The above arguments manifest that the use of Ivie’s approach can be more suitable in the
9
specific context we are working in, where we attempt to evaluate the implications of social capital on regional economic growth and performance. This economic approach to measure social
capital overcomes some of the biggest difficulties highlighted by the literature: the vagueness,
the measurement, the aggregation, the treatment of social capital as an asset in which people
can invest and the consideration of economic variables in the social capital formation process.
It can also offers additional insights in order to understand the role of one concept characterized by a multifaceted perspective better, and its use will allow for comparison with previous
results from studies which have been using more traditional measures as described above.
4.
Data and descriptive statistics
Data for the estimations are disaggregated by provinces. There are 50 provinces in Spain
excluding Ceuta and Melilla.16 They correspond to NUTS 3 in European nomenclature.
Selecting the explanatory factors of economic growth is not an easy matter. As we previously commented in the introduction, a number of references have focused on determining the
true determinants of economic growth. The only three variables which appear always significant are: initial level of income, investment rate and human capital. Sala-i-Martin (1997), in an effort
to further investigate more possible robust variables, elaborated a robustness analysis using a
modification of the extreme bounds test initially developed by Leamer (1985), concluding that
a considerable set of variables could be used as robust growth determinants.17 Unfortunately, a
measure of social capital is not included in that robustness analysis, in part because the major
of the studies incorporating social capital are more recent.
In our case, in order to explain the different levels of GPD per capita ( GDPpc) that Spanish
provinces present, we consider as an explanatory variables the three “robust variables”: the
initial level of GDP per capita ( GDPpc83), which reflects the β convergence effect18 , the private
physical capital investment ( PK ), human capital ( HK ) and, following Pérez et al. (2005), we
also include the employed population ( Employ). Last variable can play an important role in
Spain because in the last decades, but specially in the period 1995 - 2005, Spanish economic
growth has been based on a strong generation of employment with important differences
among provinces. Together with this set of variables, we introduce to the model our variable
of interest, social capital (SK ).
On the second stage of the study, we test the impact of social capital on private physical cap16 Ceuta and Melilla are not provinces but autonomous cities, with different characteristics to those of provinces
and consequently are not considered in the analysis.
17 Concretely, besides the three cited variables, Sala-i-Martin (1997) found 9 different “groups” of robust variables:
regional variables, political variables, religious variables, types of investment, primary sector production, openness
degree, type of economic organization and former Spanish Colonies.
18 β convergence reflects that those provinces in the sample less economically developed at the beginning of the
period, grow in higher degree. That idea derives from the assumption of diminishing returns of the productive
factors.
10
ital investment. One more, there is not an agreement about which are really the determinants
of this type of investment and authors studying this matter have used different explanatory
variables. Studies such as Knack and Keefer (1997) or Zak and Knack (2001) consider the price
of investment goods, which is theoretically one of the potential drivers of investment although
this variable does not appear, for instance, in Dearmon and Grier (2011), who incorporate
other macroeconomic indicators like the lag of the inflation, the lag of government spending as
% of GDP and the lag of GDP growth besides a human capital indicator, arguing that last may
present spillover effects which can affect investment.
In our case, we have considered as an explanatory variables the legal interest rate (r), a
price index (CPI ), the lag of GDP (lagGDP), the lag of private physical capital investment
(lagPK ), human capital ( HK ) and the unemployment rate (Unem). The decision of incorporating lagGDP is sustained by the argument that investment activity may be influenced by the
GDP obtained in the previous year because it could determine the saving level. Another reason
is that the decision of investing is one which has to be deliberated, so it needs time to become
reality. Both commented ideas can make that the income achieved in a certain year is not
immediately invested. By its part, lagPK is expected to be positive because investment in one
year may constitute a positive incentive in the recipient provinces, which can make them more
attractive in investment in the future periods. We find some evidence in Bernanke (1983), who
found a significant relationship between the lag of investment and investment in the following
period. Because we consider that expectations about the future evolution of the economy play
an important role on the investment decision, we include Unem as an additional regressor. The
unemployment rate has been traditionally one the main worries for Spanish population and it
could be a good indicator of how the economic environment is perceived. Unem is not only
an expectation measurer in the sense that higher levels of unemployment are expected to be
explained by economic weakness, so accordingly, investment activity will be reduced.
Because of the large impact of the construction sector in Spain during the analyzed period,
which is partly responsible for the current crisis affecting the country, we consider also using as
a dependent variable the physical private investment subtracting the amount corresponding to
the residential investment ( PKNR). Figure ?? shows that in provinces such as Málaga, Alacant
and Illes Balears, the residential component is around the 50% of the total private physical
investment. To our knowledge, there is not any literature focused on the role of social capital
in investment separating that residential component. We carry out this because in the specific
case we are dealing with, Spain, that distinction turns practically necessary. The reason is that
the “Spanish construction bubble”, which exploded in 2008, was been conformed since 1995,
after the 1993-1994 economic crisis, so an important part of the analyzed period is affected by
this fact. If social capital is one of the determinants of investment, it could be quite interesting
to determine if its effects remain significant when residential component is removed and how
11
much important the possible differences are.
Figures ?? to 7 and table 1 provide some descriptive statistics. As a previous step before the
subsequent regression analysis, it could be useful to have a look at the mentioned statistics. We
have depicted in scatter plots the different observations of the variables GDPpc, PK, PKNR
and SK. This kind of graph shows the simple correlation among variables and gives us an
idea about any potential relationship among them, besides permitting the detection of outliers
which can mar or results. Figure 2 shows a positive relation between social capital and GDP
per capita. Spearman’s ρ is 0.62, significant at 1% level. Figures 3 and 4 denote a similar
pattern. In this case, Spearman’s ρ is 0.83 for both cases, which are also significant at 1%
level. These preliminary results are valid in this previous stage but we test in the next section
through a panel data regression analysis if these results are sustained.
Finally, before closing this section, looking at the maps could be utile in order to understand how variables are distributed inside Spanish territory. In figure 5 we can observe significant disparities in the GDP per capita among provinces. In 1983, Mediterranean’s, North’s
provinces and Madrid presented the highest levels whereas South’s and interior’s provinces
presented the lowest. The same can be observe in 2005, the end of the period so, differences are
highly persistent. Figure 6, which represents the total physical capital investment by provinces,
displays a close pattern, also showing relevant and persistent differences. Again, Madrid and
coastal North’s and Mediterranean’s provinces present the highest levels. Sevilla and Málaga,
which are not among the richest provinces in terms of GDP per capita, are high-investment
provinces. We can also notice how physical capital investment are more polarized at the end
of the period. Provinces like Córdoba, Cáceres, Badajoz, Ciudad Real and Asturias loss weight
in favor of coastal provinces and Madrid. This fact leads to an immediate question: Which
factors are behind this polarization process?
Because the necessity of additional insights about the determinants of these disparities
becomes evident, this paper provides a response by considering social capital, which has not
been accounted for in previous works for Spain, at least from a province perspective and
analyzing the effects on investment too. Figure 7 depicts how the biggest stocks of social
capital in 1983 were in Madrid, Mediterranean’s and North’s provinces besides Córdoba and
Sevilla. In this case, polarization is not as evident as in physical investment, but differences are
also accused. It is remarkable how social capital declined between 1983 and 2005 in Cantabric’s
coastal provinces (A Coruña, Asturias and Cantabria) and also in Pontevedra and León. In
2005, the biggest stocks of social capital are in Madrid, Mediterranean provinces, Zaragoza,
Sevilla and Vizcaya.
Data for the estimations are provided by different institutions. In Appendix B an explanation of all variables used in the study and the data sources is available.
12
5.
5.1.
Results
Social capital and GDP per capita
Our framework for the regression analysis is a standard growth equation, including those
variables defined in Section 4. The dependent variable is GDP per capita. All variables are
expressed in logarithms. The availability of data considered for the hole period 1983 - 2005
disaggregated by provinces allows for a panel data estimation. It can provide stronger conclusions than previous studies based on cross-sectional estimations. We estimate the following
model:
GDPpcit = αi + β1 GDPpc83it + β2 PKit + β3 Employit + β4 HKit + β5 SKit + µit
(1)
All variables have been introduced in 4 and defined in Appendix B.
In order to test if data present any specification problem, heteroscedasticity, serial autocorrelation and spatial autocorrelation are tested. The first one is tested by using modified Wald’s
test considering Greene and Zhang’s (2003) suggestion, which makes that the test works properly under the assumption that errors are non-normal distributed. Serial autocorrelation is
tested with Wooldridge’s (2002) autocorrelation test and finally, Pesaran’s (2004) spatial autocorrelation test allows for testing whether our data suffer cross-sectional dependence. For all
three tests we reject the null hypothesis of no specification problem, so, we have to carry out
the estimations correcting the mentioned problems in order to provide valid statistic inference.
With the purpose of controlling the unobservable heterogeneity, fixed effects by provinces
are used. We test its convenience through the Hausman’s test. In this respect, different studies
for the Spanish case have reached dissimilar conclusions. For instance, Miguélez et al. (2011)
found that fixed effects were not important, estimating the model with random effects whereas
Peña Sánchez (2008) used fixed effects in his study. It is curious that both studies utilized the
same level of disaggregation, NUTS 2, so, it seems to be that there is not a consensus in the
literature in respect of this matter. In our case, because standard Hausman’s test does not work
properly under the aforementioned specification problems, we perform the test adopting the
refinement proposed by Wooldridge (2002), which provides valid statistical inference for these
cases. Results corroborate that fixed effects are important.
Ignoring cross-sectional correlation in the estimation of panel data models can lead to
severely biased statistical results (Hoechle, 2007). To our knowledge, cross-sectional dependance has not been tested in other panel data studies evaluating the role of social capital. Thereby, due to Pesaran’s spatial autocorrelation test denotes that our data suffer crosssectional autocorrelation, we estimate the model using Driscoll and Kraay (1998) standard errors, which are robust to all three commented specification problems. Table 2 shows the
13
results.
We incorporate social capital and the controls sequentially. In all cases, coefficients show
the expected signs and are highly significant. Consistently with economic growth theory,
the sign of the initial level of GDP per capita is negative. That means that those provinces
with lower level of GDP per capita in 1983 have experienced a higher growth. Coefficient is
small becoming evident that GDP per capita convergence process in Spain has not advanced
considerably during the sample period.19
Comparatively, the variable PK has had the biggest impact on the dependent variable in
the considered period. Concretely, a 10% increase on private physical capital generates, ceteris
paribus, a 2% increase on GDP per capita in average. This result is in line with Peña Sánchez
(2008), who obtains the same coefficient but using labor productivity as a dependent variable
and NUTS 2 level of disaggregation. The positive and significant sing of the variable HK
denotes that the role of the education on the economic performance is also quite important.
The coefficient is surprisingly high and it can be compared with PK. The variable Employ is
also positive and significant at 5%. It is not surprising that in those areas with higher levels of
employment, residents enjoy upper levels of GDP per capita.
Our variable of interest, SK, is positive and highly significant and its coefficient is the
smallest. Concretely, a 10% increase in social capital remaining the other variables constant,
yields a 0.5% increase on GDP per capita. Previous evidence for Spain is scarce. Pérez et al.
(2005), who carried out a growth accountant exercise for Spain, found a positive contribution
from social capital to growth for the period 1964 - 2001, also finding negative contributions
in certain periods20. This fact supports the idea that social capital is a significant variable for
explaining the level of economic performance inside the Spanish provinces.
5.2.
Social capital and investment
In order to determine how social capital is affecting economic performance, we test if investment could be one of the mechanisms. Authors such as (Hall and Jones, 1999; Dearmon and Grier,
2011; Zak and Knack, 2001) found a positive relationship between social capital and physical
capital investment. These authors also use, as the majority of the literature, data from surveys. We perform a similar study but using the social capital measure considered above. The
dependent variable in this case is the total private investment in physical capital (PK). The
explanatory variables have been already defined in the previous section and Appendix B provides a full explanation. Variables are expressed in logarithms except CPI, r and Unem. We
19 As we commented in
the introduction, a vast literature about the convergence process in Spain have determined
that the Spanish convergence process slowed down in the 1980’s.
20 In the cited growth accountant exercise, authors disaggregate economic growth in different explanatory factors.
This kind of exercise highlighted a positive relation between social capital and growth, showing how economic
performance was affected by social capial variations, both negatives and positives.
14
estimate the following model:
PKit = αi + β1 lagGDPit + β2 lagPKit + β3 CPIit + β4 r + β5 Unemit + β6 SKit + µit
(2)
As we mentioned, due to the importance of the residential investment in Spain in the
analyzed period, specially in coastal provinces and Illes Balears, we have developed the same
study but excluding this residential component (PKNR). Explanatory variables are exactly the
same. Thus, the estimated model is:
PKNRit = αi + β1 lagGDPit + β2 lagPKNRit + β3 CPIit + β4 r + β5 Unemit + β6 SKit + µit
(3)
Table 3 shows the estimation results. Heteroscedasticity, serial and spatial autocorrelation
tests indicate that, once more, we reject the hypothesis of no specification problems so, results
are estimated, as in the previous analysis, by using Driscoll and Kraay standard errors. Robust
Hausman’s test manifests again that fixed effects are important.
We find a positive and highly significant relationship between social capital and investment.
Specifically, when the investment considered is the total value, an increase of 10% of social
capital is corresponded by a 0.7% increase in total private physical investment. The regression
allows us for determining other important relationships. Was expected, lagPK is positive and
significant and jointly with lagGDP, has the biggest impact on the dependent variable (0.7%
and 0.5% respectively as a response to a 10% increase). It is surprising the result obtained
for r because, albeit the sign is the expected, is not significant. CPI has a positive but not
significant effect and Unem is negative and its effect is significant at 5%. HK is positive and
has a weakly significant effect. Thus, from that analysis we can extract that social capital is
relevant for explaining the investment levels in the different Spanish provinces. Its effect is
similar to human capital but statistical inference results are stronger for social capital in terms
of significance level.
Concentrating on the results for investment when the part corresponding to the residential
component is detracted, results are quite similar. The only change is that Unem becomes
no significant. It can be explained because activities not related with construction are not
linked with employment in the same degree as construction activities are.21 Nevertheless, the
sign remains negative showing that high levels of unemployment detract investment because
denote economic weakness. HK is positive and 10% significant and its effects are slightly
higher than in the previous analysis.22
Social capital is again positive and highly significant. Results show, ceteris paribus, that a
10% increase in SK generates an 0.9% increase in investment in non-residential private invest21 Traditionally,
construction sector is linked with employment because the nature of the activity.
investment activities are characterized by greater necessities of human capital.
22 Non-residential
15
ment. That coefficient, although is only moderately higher than in late analysis, has involvements in the sense that if activities which provide more aggregated value are those not related
with construction sector, this finding may imply that in provinces where the cited sector has
a smaller weight in the economy, the role of social capital is more substantial than in other
provinces where construction sector is one of the pillars of the economy.
In this part of the paper we have highlighted that social capital is one of the drivers of
private physical capital investment. There is no previous evidence for the Spanish case, so
comparison with preceding results is not possible. Due to private physical capital investment
is, according to the first result of the paper, the most relevant explanatory factor of GDP per
capita, the result obtained in this part is, from our point of view, quite interesting. It supports
the hypothesis that the increase in the goodness level in the different provinces depends on
their capacity for attracting investment, which can generate more activity, employment and
wealth. So, differences in social capital endowments among provinces can be one of the factors
which determine the large differences existing in our period of reference.
5.3.
Robustness analysis
In order to test whether the above results are robust we perform a jackknife estimation, which
is a common non-parametric technic. It consists of running regressions removing one different
province in the sample each time so, in our case, 50 different regressions are performed. From
these estimated results, a distribution of the variance is constructed. Table 4 reports the results
for all three regressions.
For the case of social capital and GDP per capita, significance of the explanatory variables
does not vary in any case. When jackknife is applied to estimate social capital and investment, there are small changes in some variables. For SK, significance drops from 1% to 5%.
HK increases its significance until 5% whereas the variable r, which was not significant in the
standard analysis, is now significant at 5% level when the dependent is PK and remains no
significant for PKNR. This result shows that interest rate is important when investing in residential assets but is not relevant for non-residential investment. Unem loses any significance
when the dependent variable is PK and remains no significant for PKNR. The rest of the
variables does not suffer any variance.
The jackknife technic has showed that the results in the first part of the paper were robust
and also has corrected biases presented in the second block of estimations. The important idea
we have to extract from these results is that social capital is robust in both cases.
16
6.
Concluding remarks
Spanish provinces have historically presented considerable disparities in terms of GDP per
capita. Although differences reduced significantly during the period 1955 - 1980, literature
agrees that this process of convergence came to an end in the decade of 1980. Over the
last few years there, has emerged a growing interest in determining whether social capital has an impact on economic growth. The encouraging results found by authors such as
Putnam et al. (1994); Keefer and Knack (1997); Beugelsdijk and Van Schaik (2005) for the international case drive us to analyze if social capital is behind the commented disparities in the
Spanish provinces.
Data on social capital have been traditionally based on surveys. The availability of a new
data base of social capital, which provides data for the period 1983 - 2005 at province level,
enabled us to estimate the results using a panel data approach. Albeit panel data studies
are growing up, they are still scarce for the study of these “social features” due to the data
shortage commented along the study. The social capital measure utilized, not only is available
either with higher degree of disaggregation and a wider time span than the classic ones, but
also is a measure which solves some of the problems highlighted by the literature in terms of
measure, aggregation and the way the measure is constructed besides the elements considered
in its construction.
Another contribution of this article is the methodology. To our knowledge, previous studies
for the Spanish case did not control for the presence of cross-sectional dependence. Neglecting
that problem may lead to invalid statistic inference. After we noticed that our data presented
cross-sectional dependence, we used Driscoll and Kraay standard errors for the estimations.
Estimating using that refinement corrects heteroscedasticity and both serial and spatial autocorrelation.
According to our results, we determine in the first part of the study that social capital has
a positive influence on the level of GDP per capita of Spanish provinces. This result is highly
relevant in the sense that confirms that social capital is, among other factors, behind the differences presented by the Spanish provinces in terms of GDP per capita. That disparities are
characterized by its high persistence along time. The same occurs with social capital, confirming the idea developed by Torcal and Montero (2000), who acknowledged in their analysis for
Spain the difficulties to escape from a low social capital endowment situation. We have studied
a twenty-three year period and maybe is not enough time to reverse the cited circumstance.
These results might have interesting policy implications in the sense that if social capital is one
of the mechanisms to achieve a higher stage of economic performance, policies should pursue
the generation of greater endowments of social capital in those provinces where this asset is
relatively scarcer.
17
A second conclusion we can extract from this paper is the importance of social capital to
foster investment. We have shown that investment is the most relevant factor in the increase
of the output per capita. Investment is an activity which needs trust. We all know that the
major of the investment activities are made borrowing financial resources. The presence of
social capital in a given society or region makes easier and cheaper this kind of activities.
The theory of social capital declares the importance of the social features in the reduction of
transaction costs. If banks can save costs in supervision and checking the reliability of clients,
the last can obtain cheaper credit. Trust also can extend the relationships between banks and
clients and this fact induces lower transaction cost in future economic transactions. So, in the
current economic context in which credit does not flow as a few years ago, social capital might
be an additional instrument to recover the credit, specially in those activities not related with
construction, and drive the economy ahead in the following years.
The enormous importance of the construction sector in Spain, especially in the last decade
of the considered period, leads us to disaggregate the investment extracting the residential
component. Effects of social capital are quite similar but, when we detach the last component
the coefficient is higher and the significance is prevailed. Thereby, in those areas where investment in residential assets is more relevant, the effects of social capital fostering investment are
slimly lower.
To sum up, this paper has evidenced the importance of social capital as a considerable
factor for explaining the economic development and the persistent differences among Spanish provinces. We have also reported evidence that investment may be one of the candidate
transmitters of the stimulant effects from social capital to GDP per capita. Additionally, using
the social capital measure that Ivie provides we give an answer to some problems related with
the treatment of social capital besides correcting some econometric issues ignored in other
contributions in the matter.
Our future research agenda is comprised basically by two immediate goals. First, we try to
extend the present analysis at country level. It could be interesting in comparison with other
cross-country studies that use cross-sectional samples using data from WVS and EVS. Second,
given the controversy around the explanatory factors of economic growth and the amount
of possible variables as a regressors through parametric approaches (Sala-i-Martin, 1997), we
should consider some additional flexible techniques within the nonparametric econometrics
field. These remain as open and intriguing questions to be explored in immediate research
initiatives.
18
Appendix A. An economic approach to measuring social capital. Some basic ideas
The social capital measure we use is based on three initial hypothesis:
1. Cooperation in a society is favored by the economic incentives derived from a higher
expected incomes, result of a continuous growth.
2. The incentives for cooperation are reinforced/weaken by two factors:
• The effective opportunity of participation in the final incomes.
• The culture of reciprocity fulfillment.
3. The effects of cooperation are increased in societies with a high density network.
The investment in social capital is denoted by ( Is ). A member in a given society invests
in social capital if the expected benefits of cooperation are positive ( Is > 0). If the economy
follows a continuous growth trend, the income achieved is higher than the simple reposition
of the production factors and, moreover, the results are crescent in time.
y > rk + w̄
(4)
where y is the income, rk is the cost of the physical capital and w̄ is the salary of labor.
Other assumptions of this approach are:
• The individuals observe the difference in the incomes that they obtain under certain time
and place conditions and other conditions less favorable.
• This difference determines the incentives for cooperation and trust (investment in social
capital).
An individual incurs in two types of costs to obtain incomes:
• Cost of contribution with productive resources (we expect a retribution equal to the
reposition costs).
• Cost in terms of effort of cooperation inside an incomplete information environment.
Cost of cooperation include both time and psychical costs.
Following the above statements, benefits are expressed as:
π = y − (rk + w̄) − w̄C ( Is )
19
(5)
where C ( Is ) is the cost of cooperation measured in wage terms.
If one individual owns social capital, she/he would expect to obtain additional income
using it for her/his economic transactions. The T horizon defines her/his expectations according to the duration of her/his economic relations inside a society or network. If her/his
expectations are not fulfilled, her/his social capital will be depreciated at ρ rate.
In a given moment, our representative individual invests in social capital if,
T
π=
1
∑ (1 + ρ)t (yt (1 − G) − rkt − w̄t (1 + C( Ist ))) > 0
(6)
t =0
where (1 − G ) is the Gini’s Index and measures the inequality in the society.
The next step is to focus on the services that social capital provides, ( f ks). The capability
of social capital to contribute to an increase of total output depends on its capacity to generate
services, i.e. a reduction in transaction costs.
(7)
f ksi = ci ksi
where ci is the degree of connection of the network and ksi is the social capital stock of the
individual i. If a given individual is perfectly connected, it implies ci = 1, the contribution of
social capital is maximum. The opposite holds for ci = 0. The economic value of the services
of social capital is defined in terms of its use cost ui .
(8)
u i = ρi + di
where ρi is the financial opportunity cost, and di is the depreciation cost.
Therefore, the value of the services of social capital can be expressed as:
vksi = ui f ksi = (ρi + di)ci ksi
(9)
The final step is the aggregation of the social capital of the individuals. Services cannot
be directly added because of their varying nature. Therefore, authors follow a multiplying
process, weighting each social capital unit by its own use cost weighted respect the total use
cost. The weight is calculated as:
vi =
vksi
vks j
(10)
∑N
j=1
Regarding the above consideration, the services of social capital are aggregated as follows:
N
N
i=1
i=1
vi vi
KS = N ∏ f ksvi
i = N ∏ c i ksi
20
(11)
Appendix B.
Variables and data sources
• GDPpc: Real GDP per capita (e). Serie deflated using 2000 as base year. [Source: Instituto
Nacional de Estadística (INE)].
• GDPpc83: Real GDP per capita in 1983 (e). Year 2000 used as a base year. [Source:
Instituto Nacional de Estadística (INE)].
• PK: Private physical capital investment (Thousand e). Serie deflated using 2000 as base
year. [Source: BBVA Foundation].
• PKNR: Private physical capital investment detracting the residential component (Thousand e). Serie deflated using 2000 as base year. [Source: BBVA Foundation].
• Employ: Employed population (persons). [Source: Instituto Nacional de Estadística
(INE)].
• HK: Rate of active working population at least with secondary studies. Values between
(0 - 1). [Source: Instituto Nacional de Estadística (INE) and Ivie-Bancaja].
• SK: Social capital services stock. Data are provided by Ivie’s (1964 - 2001) database and
its updating until 2005. Both series have been connected using Spain 1983 = 100 as a
reference point. [Source: Ivie]
• CPI: Consumer Price Index. Year 2001 = 100. [Source: Instituto Nacional de Estadística
(INE)].
• Unem: Unemployment rate in percentage. [Source: Instituto Nacional de Estadística
(INE)].
• r: Legal Interest Rate in percentage. [Source: Instituto Nacional de Estadística (INE)].
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25
Table 1: Descriptive Statistics
26
Province
Almería
Cádiz
Córdoba
Granada
Huelva
Jaén
Málaga
Sevilla
Huesca
Teruel
Zaragoza
Asturias
Illes Balears
Las Palmas
Sta. Cruz de Tenerife
Cantabria
Ávila
Burgos
León
Palencia
Salamanca
Segovia
Soria
Valladolid
Zamora
Albacete
Ciudad Real
Cuenca
Guadalajara
Toledo
Barcelona
Girona
Lleida
Tarragona
Alacant
Castelló
València
Badajoz
Cáceres
A Coruña
Lugo
Ourense
Pontevedra
Madrid
Murcia
Navarra
Álava
Guipúzcoa
Vizcaya
La Rioja
GDPpc
1983
2005
7,290
15,330
7,750
13,010
6,990
11,070
6,330
11,590
7.980
13,960
7,000
10,550
7,600
13,390
6,980
13,160
8,590
16,810
11,500
17,760
9,720
18,420
8,360
14,750
12,080
18,280
11,290
15,810
9,440
14,590
9,920
16,360
7,030
13,440
10,690
19,330
8,860
14,890
10,230
16,720
7,010
14,140
9,270
17,460
9,250
16,230
9,950
18,100
6,310
12,910
6,880
12,610
7,580
13,150
6,840
12,990
8,440
14,680
7,520
13,440
10,760
19,860
11,820
19,950
12,090
19,490
13,510
20,110
9,720
14,450
10,930
18,130
9,250
15,290
5,010
11,560
5,230
11,590
9,170
14,400
9,320
13,270
6,290
12,090
7,980
13,550
12,470
22,120
9,050
13,810
12,700
21,450
16,800
23,780
12,440
21,740
12,360
20,970
10,320
18,460
PK
1983
823,228
1,610,586
953,348
1,223,938
579,276
650,704
2,181,167
1,798,013
572,749
323,480
1,343,488
1,875,148
968,191
1,242,225
1,399,457
1,010,428
325,410
813,416
1,267,558
458,520
739,047
344,360
250,870
840,467
385,672
436,873
971,295
438,970
395,289
907,573
6,307,167
1,770,484
898,672
2,843,645
2,817,847
872,711
3,506,459
936,581
1.205,645
1,632,378
660,475
520,469
1,276,661
8,431,737
1,823,147
763,947
692,147
928,477
1,830,828
520,019
2005
2,740,018
4,532,782
2,410,765
3,078,974
2,272,457
2,024,585
7,541,517
6,702,257
1,705,614
967,757
4,530,717
4,686,708
5,926,796
4,089,235
5,512,771
2,904,759
752,477
2,246,669
2,096,107
946,299
1,512,273
1,034,366
400,218
2,470,537
799,689
1,622,143
2,087,364
958,033
1,214,615
3,258,054
24,876,603
4,107,277
2,201,314
6,197,371
7,646,879
2,717,139
9,866,996
2,000,653
1,857,649
5,220,250
1,342,710
1,213,252
3,526,668
34,926,663
5,861,135
3,915,349
2,015,551
3,813,429
6,368,429
1,825,959
PKNR
1983
2005
424,872
1,825,803
867,345
3,023,796
586,944
1,862,800
569,212
1,998,060
299,629
1,506,026
399,429
1,408,666
802,142
4,014,079
1,008,679
4,888,329
388,995
1,046,041
250,341
815,867
787,386
3,430,601
1,348,882
3,135,047
523,996
3,713,662
845,945
3,193,413
863,002
3,367,001
638,457
1,921,967
187,130
542,461
532,728
1,441,889
829,685
1,840,156
312,101
637,787
537,380
901,996
207,518
692,543
170,881
283,739
510,532
1,825,826
273,992
551,533
280,263
1,334,066
758,337
1,412,041
239,141
853,040
271,827
747,728
551,557
2,309,667
4,920,395
17,751,762
868,079
2,593,299
716,152
1,468,780
2,264,170
3,196,789
1,040,362
4,286,020
499,910
1,985,540
2,121,432
6,871,262
660,865
1,582,951
973,895
1,120,060
1,020,453
3,625,406
472,655
945,050
360,958
883,513
743,921
2,429,082
4,979,612
23,942,998
821,142
4,057,558
547,693
2,775,697
472,142
1,387,599
775,098
2,480,161
1,645,878
4,622,732
290,827
1,259,599
Employ
1983
2005
116,930
312,950
238,530
503,630
186,250
328,600
184,600
361,780
102,730
204,080
158,880
266,550
285,280
636,930
341,850
820,680
74,780
97,650
50,100
62,200
265,050
443,880
361,580
451,400
220,700
504,450
201,300
496,100
196,130
451,030
161,380
261,400
56,980
70,100
116,680
170,880
190,800
207,030
52,580
77,600
105,330
156,250
52,780
74,450
29,750
40,050
136,530
247,580
74,680
76,530
98,280
175,100
130,380
207,700
66,150
84,900
38,600
96,450
152,900
276,680
1,355,080
2,627,930
178,200
359,730
130,530
193,280
158,600
356,100
363,950
813,580
143,100
266,550
618,850
1,171,250
178,280
282,780
130,480
175,250
399,530
520,330
179,830
154,830
177,180
138,250
340,330
441,400
1,449,530
3,067,550
267,350
619,280
162,050
290,850
83,450
149,900
217,850
340,780
353,430
543,380
86,050
150,080
HK
1983
2005
0.13
0.21
0.14
0.29
0.12
0.32
0.14
0.32
0.13
0.36
0.11
0.39
0.17
0.35
0.15
0.32
0.15
0.28
0.12
0.31
0.18
0.24
0.14
0.27
0.17
0.36
0.15
0.33
0.13
0.27
0.17
0.25
0.12
0.35
0.19
0.27
0.16
0.28
0.15
0.20
0.12
0.22
0.17
0.29
0.16
0.38
0.16
0.26
0.12
0.21
0.14
0.40
0.16
0.33
0.16
0.38
0.13
0.30
0.14
0.35
0.21
0.24
0.21
0.34
0.12
0.21
0.19
0.27
0.17
0.35
0.13
0.38
0.20
0.33
0.16
0.42
0.14
0.42
0.11
0.30
0.09
0.36
0.09
0.29
0.14
0.37
0.20
0.21
0.16
0.31
0.21
0.23
0.20
0.23
0.18
0.20
0.20
0.21
0.16
0.23
SK
1983
0.45
0.64
1.14
0.58
0.23
0.31
0.85
1.09
0.60
0.27
2.29
2.66
1.53
0.76
0.82
1.11
0.45
0.61
1.26
0.21
0.69
0.43
0.19
0.68
0.51
0.97
0.39
0.51
0.14
0.84
13.16
1.82
1.29
0.87
2.00
0,74
4.14
0.53
0.69
3.72
0.85
0.90
3.38
33.94
1.34
1.06
0.72
1.71
3.30
0.64
2005
9.60
7.84
7.86
8.50
2.87
2.83
1.53
25.76
3.44
1.85
30.28
13.17
37.29
19.34
16.79
11.32
2.53
10.26
5.96
2.16
5.99
2.85
1.83
12.68
0.98
6.12
4.08
2.77
7.82
10.26
219.97
26.06
9.34
17.80
49.50
20.11
77.51
4.78
3.90
22.94
4.36
2.87
18.70
259.10
31.92
14.11
8.05
18.51
25.36
9.10
Unem
1983
2005
15.20
9.16
28.60
17.65
18.90
14.78
23.05
12.91
21.88
15.88
14.88
15.92
21.20
11.66
29.20
13.86
10.10
6.89
5.20
4.68
16.25
5.76
13.73
10.24
13.88
7.21
21.38
12.83
17.83
10.50
12.70
8.51
11.85
8.82
15.90
6.73
10.90
10.82
15.23
7.50
15.60
9.09
9.70
6.80
9.75
5.16
18.28
9.23
9.85
10.00
15.30
10.00
19.10
10.58
8.35
6.49
21.00
7.05
10.55
9.12
24.60
6.97
9.68
7.33
5.48
5.89
15.48
7.06
19.10
9.61
12.00
7.31
18.40
8.59
18.40
17.51
13.55
12.98
11.58
9.86
6.20
6.67
7.53
10.49
10.93
11.02
17.55
6.80
16.68
8.01
15.80
5.65
14.20
7.09
19.35
5.66
22.03
8.45
11.13
6.18
GDP per capita in (e). PK and PKNR in thousands (e). HK is 0 - 1 fenced. SK is the volume of social capital services. CPI 2001 = 100. Unem is expressed in percentage.
CPI
1983
49.43
49.08
45.72
45.03
46.96
46.16
52.44
49.40
48.33
47.11
53.81
45.67
48.18
44.45
40.56
49.25
52.56
50.78
45.45
48.44
51.17
46.46
54.68
46.15
48.52
47.04
45.53
52.74
48.09
48.63
45.93
43.09
42.94
45.37
52.09
51.38
50.50
48.76
46.04
46.45
47.23
46.52
43.68
48.13
48.81
42.01
45.39
47.28
47.61
44.11
2005
114.86
113.01
112.67
113.05
112.07
113.07
114.34
112.75
113.16
113.89
113.42
113.26
113.22
110.21
109.75
112.55
113.08
113.15
113.38
112.17
111.69
112.70
111.94
112.52
112.97
114.28
113.49
112.36
113.69
113.43
115.75
115.93
114.63
112.08
113.04
113.20
113.52
110.60
112.19
114.72
113.20
112.87
113.77
113.38
114.80
113.93
113.72
112.75
113.79
114.24
Table 2: Economic growth determinants 1983–2005
(Intercept)
GDP83
GDPpc
GDPpc
GDPpc
GDPpc
GDPpc
2.503∗∗∗
(0.055)
−0.262∗∗∗
(0.047)
−3.560∗∗∗
(0.369)
−0.090∗∗∗
(0.018)
0.425∗∗∗
(0.025)
−3.182∗∗∗
(0.377)
−0.095∗∗∗
(0.019)
0.316∗∗∗
(0.032)
0.223∗∗∗
(0.055)
−1.843∗∗∗
(0.317)
−0.044∗∗∗
(0.014)
0.256∗∗∗
(0.027)
0.187∗∗∗
(0.363)
0.213∗∗∗
(0.025)
−0.648∗∗
(0.309)
−0.058∗∗∗
(0.015)
0.204∗∗∗
(0.023)
0.090∗∗
(0.043)
0.193∗∗∗
(0.029)
0.054∗∗∗
(0.001)
1150
31.55∗∗∗
0.13
1150
543.20∗∗∗
0.77
1150
711.68∗∗∗
0.79
1150
963.63∗∗∗
0.84
1150
632.94∗∗∗
0.85
χ = 1149.77∗∗∗
F = 231.24∗∗∗
F = 26.78∗∗∗
F = 25.31∗∗∗
PK
Employ
HK
SK
N
F
WhitinR2
Wald’s Heteroscedasticity Test
Wooldridge’s Serial Autocorrelation Test
Pesaran’s Spatial Autocorrelation Test
Robust Hausman’s Test
∗ , ∗∗
and ∗∗∗ denote significance at 10%, 5%, and 1% significance levels, respectively. Standard errors reported
in brackets.
All test referred to the last not corrected regression. Estimations reported on the table are corrected by using
Driscoll and Kraay standard errors.
27
Table 3: Social capital and investment 1983–2005
(Intercept)
PK
PKNR
4.609∗∗∗
4.689∗∗∗
(0.696)
−0.003
(0.012)
0.596∗∗∗
(0.161)
(0.733)
−0.008
(0.010)
0.583∗∗∗
(0.158)
0.691∗∗∗
(0.048)
r
lagGDP
lagPK
0.076∗
(0.041)
−0.004∗∗
(0.002)
0.002
(0.011)
0.074∗∗∗
(0.022)
0.676∗∗∗
(0.046)
0.104∗
(0.061)
−0.002
(0.002)
−0.001
(0.015)
0.090∗∗∗
(0.029)
1100
772.31∗∗∗
0.88
χ = 259.03∗∗∗
F = 93.41∗∗∗
F = 21.18∗∗∗
F = 1.7e + 05∗∗∗
1100
487.23∗∗∗
0.86
χ = 643.42∗∗∗
F = 215.75∗∗∗
F = 26.60∗∗∗
F = 2.4e + 05∗∗∗
lagPKNR
HK
Unem
CPI
SK
N
F
WhitinR2
Wald’s Heteroscedasticity Test
Wooldridge’s Serial Autocorrelation Test
Pesaran’s Spatial Autocorrelation Test
Robust Hausman’s Test
∗ , ∗∗ and ∗∗∗ denote significance at 10%, 5%, and 1% significance levels, respectively.
All test are referred to the not corrected model. Estimations reported on the
table are corrected by using Driscoll and Kraay standard errors.
28
Table 4: Robustness analysis. Jackknife estimations
(Intercept)
GDP83
PK
Employ
HK
GDPpc
PK
PKNR
−0.648
(0.406)
−0.058∗∗∗
(0.013)
0.204∗∗∗
(0.028)
0.090∗∗
(0.044)
0.193∗∗∗
(0.029)
4.609∗∗∗
4.689∗∗∗
(0.412)
r
lagGDP
lagPK
(0.383)
0.076∗∗
(0.036)
−0.008∗∗
(0.004)
0.583∗∗∗
(0.127)
0.690∗∗∗
(0.030)
lagPKNR
SK
0.054∗∗∗
(0.011)
−0.004∗
(0.003)
0.002
(0.006)
0.074∗∗
(0.031)
N
F
WhitinR2
1150
152.00∗∗∗
0.85
1100
899.52∗∗∗
0.88
Unem
CPI
∗ , ∗∗
0.104∗∗
(0.047)
−0.003
(0.004)
0.596∗∗∗
(0.146)
0.676∗∗∗
(0.032)
−0.002
(0.004)
−0.001
(0.007)
0.090∗∗
(0.039)
1100
734.65∗∗∗
0.86
and ∗∗∗ denote significance at 10%, 5%,
and 1% significance levels, respectively.
29
Figure 1: Investment components by provinces. Mean values 1983 - 2005
A Coruña
Alacant
Albacete
Almería
Asturias
Badajoz
Barcelona
Burgos
Cantabria
Castelló
Ciudad Real
Cuenca
Cáceres
Cádiz
Córdoba
Girona
Granada
Guadalajara
Guipúzcoa
Huelva
Huesca
Illes Balears
Jaén
La Rioja
Las Palmas
León
Lleida
Lugo
Madrid
Murcia
Málaga
Navarra
Ourense
Palencia
Pontevedra
Salamanca
Segovia
Sevilla
Soria
Sta. Cruz de Tenerife
Tarragona
Teruel
Toledo
Valladolid
València
Vizcaya
Zamora
Zaragoza
Álava
Ávila
0
5.000.000
10.000.000
Non−residential investment
30
Residential investment
15.000.000
Figure 2: GDP per capita and social capital
2.5
1.5
Log GDP pc
3.5
Scatterplot
Spearman’s rho = 0.62 Prob > |t| = 0.0000
0
2
Log SK
4
6
Figure 3: Physical capital investment and social capital
11
13
Log PK
15
17
Scatterplot
Spearman’s rho = 0.83
0
2
Log SK
Prob > |t| = 0.0000
4
6
Figure 4: Physical capital investment (non-residential) and social capital
12
14
Log PKNR
16
18
Scatterplot
Spearman’s rho = 0.83
0
2
Log SK
31
4
Prob > |t| = 0.0000
6
Figure 5: GDP per capita by provinces
a) 1983
GUIPUZCOA
VIZCAYA
ASTURIAS
A CORUÑA
CANTABRIA
LUGO
ALAVA
LEON
PONTEVEDRA
NAVARRA
BURGOS
HUESCA
LA RIOJA
OURENSE
PALENCIA
GIRONA
ZAMORA
VALLADOLID
LLEIDA
SORIA
ZARAGOZA
TARRAGONA
SEGOVIA
GUADALAJARA
SALAMANCA
AVILA
TERUEL
MADRID
CASTELLON
CUENCA
TOLEDO
CACERES
BARCELONA
VALENCIA
ILLES BALEARS
ALBACETE
CIUDAD REAL
BADAJOZ
ALICANTE
CORDOBA
HUELVA
MURCIA
JAEN
SEVILLA
GRANADA
ALMERIA
+ 10.800
9.200 − 10.800
7.300 − 9.200
5.000 − 7.300
MALAGA
CADIZ
ST. CRUZ DE TENERIFE
LAS PALMAS
b) 2005
VIZCAYA
GUIPÚZCOA
ASTURIAS
A CORUÑA
CANTABRIA
LUGO
ÁLAVA
NAVARRA
LEÓN
PONTEVEDRA
BURGOS
LA RIOJA
PALENCIA
OURENSE
HUESCA
ZAMORA
SORIA
CÁCERES
BARCELONA
ZARAGOZA
SEGOVIA
TARRAGONA
GUADALAJARA
SALAMANCA
ÁVILA
GIRONA
LLEIDA
VALLADOLID
TERUEL
MADRID
CASTELLÓ
CUENCA
TOLEDO
VALÈNCIA
ILLES BALEARS
CIUDAD REAL
ALBACETE
BADAJOZ
ALACANT
CÓRDOBA
JAÉN
MURCIA
HUELVA
SEVILLA
GRANADA
ALMERÍA
MÁLAGA
CÁDIZ
ST. CRUZ DE TENERIFE
LAS PALMAS
32
+ 18.300
14.800 − 18.300
13.300 − 14.800
10.500 − 13.300
Figure 6: Physical capital investment by provinces
b) 1983
GUIPÚZCOA
VIZCAYA
ASTURIAS
A CORUÑA
CANTABRIA
LUGO
ÁLAVA
NAVARRA
LEÓN
PONTEVEDRA
BURGOS
HUESCA
LA RIOJA
OURENSE
GIRONA
PALENCIA
ZAMORA
VALLADOLID
LLEIDA
SORIA
BARCELONA
ZARAGOZA
SEGOVIA
TARRAGONA
GUADALAJARA
SALAMANCA
TERUEL
ÁVILA
MADRID
CASTELLÓ
CUENCA
TOLEDO
CÁCERES
VALÈNCIA
CIUDAD REAL
ILLES BALEARS
ALBACETE
BADAJOZ
ALACANT
CÓRDOBA
MURCIA
JAÉN
HUELVA
SEVILLA
GRANADA
+ 1.600.000
930.000 − 1.600.000
580.000 − 930.000
250.000 − 580.000
ALMERÍA
MÁLAGA
CÁDIZ
ST. CRUZ DE TENERIFE
LAS PALMAS
b) 2005
VIZCAYA
GUIPÚZCOA
ASTURIAS
A CORUÑA
CANTABRIA
LUGO
ÁLAVA NAVARRA
LEÓN
PONTEVEDRA
BURGOS
HUESCA
LA RIOJA
OURENSE
GIRONA
PALENCIA
ZAMORA
LLEIDA
VALLADOLID
SORIA
TARRAGONA
SEGOVIA
SALAMANCA
GUADALAJARA
ÁVILA
CÁCERES
BARCELONA
ZARAGOZA
TERUEL
MADRID
CASTELLÓ
CUENCA
TOLEDO
VALÈNCIA
ILLES BALEARS
CIUDAD REAL
ALBACETE
BADAJOZ
ALACANT
CÓRDOBA
JAÉN
MURCIA
HUELVA
SEVILLA
GRANADA
MÁLAGA
CÁDIZ
ST. CRUZ DE TENERIFE
LAS PALMAS
33
ALMERÍA
+ 4.700.000
2.600.000 − 4.700.000
1.800.000 − 2.600.000
400.000 − 1.800.000
Figure 7: Social capital by provinces
a) 1983
VIZCAYA
ASTURIAS
A CORUÑA
GUIPÚZCOA
CANTABRIA
LUGO
ÁLAVA
NAVARRA
LEÓN
BURGOS
PONTEVEDRA
OURENSE
HUESCA
LA RIOJA
PALENCIA
GIRONA
ZAMORA
VALLADOLID
LLEIDA
SORIA
ZARAGOZA
TARRAGONA
SEGOVIA
SALAMANCA
GUADALAJARA
ÁVILA
TERUEL
MADRID
CASTELLÓ
CUENCA
TOLEDO
CÁCERES
BARCELONA
VALÈNCIA
ILLES BALEARS
ALBACETE
CIUDAD REAL
BADAJOZ
ALACANT
CÓRDOBA
HUELVA
MURCIA
JAÉN
+ 1.77
0.94 − 1.77
0.69 −0.94
0.48 −0.69
0.14 −0 .48
SEVILLA
GRANADA
ALMERÍA
MÁLAGA
CÁDIZ
ST. CRUZ DE TENERIFE
LAS PALMAS
b) 2005
VIZCAYA
ASTURIAS
A CORUÑA
GUIPÚZCOA
CANTABRIA
LUGO
ÁVILA
NAVARRA
LEÓN
BURGOS
PONTEVEDRA
OURENSE
PALENCIA
HUESCA
LA RIOJA
GIRONA
ZAMORA
VALLADOLID
LLEIDA
SORIA
ZARAGOZA
TARRAGONA
SEGOVIA
SALAMANCA
GUADALAJARA
ÁVILA
CÁCERES
BARCELONA
TERUEL
MADRID
CASTELLÓ
CUENCA
TOLEDO
VALÈNCIA
ILLES BALEARS
CIUDAD REAL
ALBACETE
BADAJOZ
ALACANT
CÓRDOBA
MURCIA
JAÉN
HUELVA
SEVILLA
GRANADA
MÁLAGA
CÁDIZ
ST. CRUZ DE TENERIFE
LAS PALMAS
34
ALMERÍA
+ 24.15
12.00 − 24.15
7.83 − 12.00
2.87 − 7.83
0.98 − 2.87